Experiment Tracking
The systematic recording of ML experiment parameters, metrics, and artifacts for comparison and reproducibility.
Overview
Experiment tracking is the practice of systematically logging all information about machine learning experiments — hyperparameters, training metrics, model architectures, data versions, code versions, and output artifacts. This enables comparison across experiments and ensures reproducibility.
Key Details
Popular tools include Weights & Biases, MLflow, Neptune, and Comet. These platforms provide dashboards for visualizing training curves, comparing runs, and sharing results with teams. Good experiment tracking is essential for making informed decisions about which models to deploy and understanding why certain approaches work better than others.